Grain segmentation of multi-angle petrographic thin section microscopic images

摘要

Grain segmentation of petrographic thin section microscopic (TSM) images is the first step for computer aided mineral identification and rock naming. The TSM images contain a large number of mineral grains and the differences among adjacent grains are usually ambiguous, which makes current segmentation technologies inefficient. In this paper, we take advantage of multi-angle TSM images and propose a method for grain segmentation. Accordingly, the method consists of two steps, in the first step, we enhance the SLIC algorithm to handle multi-angle images and produce the initial superpixels. In the second step, multiple features are extracted for comprehensive description of the superpixels, and dissimilarities between superpixels are measured according to the extracted features. Then the multi-angle region merging algorithm is employed to merge similar adjacent superpixels and get the final segmentation results. Experimental results demonstrate both the effectiveness and potential of the proposed method for grain segmentation of TSM images.